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Record W4389051962 · doi:10.1080/0164212x.2023.2285319

Impact of COVID-19 Pandemic on the Occupation and Mental Health of Students: A Scoping Review

2023· review· en· W4389051962 on OpenAlexaff
Catherine Rose Talastas, Parisa Ghanouni

Bibliographic record

VenueOccupational Therapy in Mental Health · 2023
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMental health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicinePsychiatryVirologyDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has caused an inevitable impact on the health and well-being of young adults. This study aims to gather the literature on how the pandemic impacted the occupation considering the mental health experiences of anxiety and depression of young adult students. A scoping review with thematic analysis was conducted in three electronic databases OVID Medline, PubMed, and CINAHL and the Journal of Occupational Science. Included articles (a) were in the English language, (b) explained the impact of the COVID-19 pandemic on doing occupations or activities, (c) explained the impact of the COVID-19 pandemic on mental health, including anxiety and/or depression, and (d) have participants who are all or mostly are students aged 18–25 years old. Findings show that the pandemic has disrupted several occupations, such as school, work, sleep, leisure, physical activity, and social participation, leading to adaptation. The studies reviewed indicated a positive and negative correlation between mental health and occupation. Findings imply the benefit for occupational therapists to understand the impact of the pandemic on occupation and mental health. Considering the findings, more peer-reviewed and rigorous studies are needed to evaluate further how the pandemic influences individuals’ overall health and well-being. This will prepare society to overcome future health predicaments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.390
GPT teacher head0.635
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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